OpenRaiser/Intern-Atlas huggingface dataset:
Managing Reliability Skew in DNA Storage
Venue: arXiv cs.ET 2022
Year: 2022

DNA is emerging as an increasingly attractive medium for data storage due to a number of important and unique advantages it offers, most notably the unprecedented durability and density. While the technology is evolving rapidly, the prohibitive cost of reads and writes, the high frequency and the peculiar nature of errors occurring in DNA storage pose a significant challenge to its adoption. In this work we make a novel observation that the probability of successful recovery of a given bit from any type of a DNA-based storage system highly depends on its physical location within the DNA molecule. In other words, when used as a storage medium, some parts of DNA molecules appear significantly more reliable than others. We show that large differences in reliability between different parts of DNA molecules lead to highly inefficient use of error-correction resources, and that commonly used techniques such as unequal error-correction cannot be used to bridge the reliability gap between different locations in the context of DNA storage. We then propose two approaches to address the problem. The first approach is general and applies to any types of data; it stripes the data and ECC codewords across DNA molecules in a particular fashion such that the effects of errors are spread out evenly across different codewords and molecules, effectively de-biasing the underlying storage medium. The second approach is application-specific, and seeks to leverage the underlying reliability bias by using application-aware mapping of data onto DNA molecules such that data that requires higher reliability is stored in more reliable locations, whereas data that needs lower reliability is stored in less reliable parts of DNA molecules. We show that the proposed data mapping can be used to achieve graceful degradation in the presence of high error rates, or to implement the concept of approximate storage in DNA.
Science Checker: Extractive-Boolean Question Answering For Scientific Fact Checking
Venue: arXiv cs.CL 2022
Year: 2022

With the explosive growth of scientific publications, making the synthesis of scientific knowledge and fact checking becomes an increasingly complex task. In this paper, we propose a multi-task approach for verifying the scientific questions based on a joint reasoning from facts and evidence in research articles. We propose an intelligent combination of (1) an automatic information summarization and (2) a Boolean Question Answering which allows to generate an answer to a scientific question from only extracts obtained after summarization. Thus on a given topic, our proposed approach conducts structured content modeling based on paper abstracts to answer a scientific question while highlighting texts from paper that discuss the topic. We based our final system on an end-to-end Extractive Question Answering (EQA) combined with a three outputs classification model to perform in-depth semantic understanding of a question to illustrate the aggregation of multiple responses. With our light and fast proposed architecture, we achieved an average error rate of 4% and a F1-score of 95.6%. Our results are supported via experiments with two QA models (BERT, RoBERTa) over 3 Million Open Access (OA) articles in the medical and health domains on Europe PMC.
Empowering Next POI Recommendation with Multi-Relational Modeling
Venue: arXiv cs.IR 2022
Year: 2022

With the wide adoption of mobile devices and web applications, location-based social networks (LBSNs) offer large-scale individual-level location-related activities and experiences. Next point-of-interest (POI) recommendation is one of the most important tasks in LBSNs, aiming to make personalized recommendations of next suitable locations to users by discovering preferences from users' historical activities. Noticeably, LBSNs have offered unparalleled access to abundant heterogeneous relational information about users and POIs (including user-user social relations, such as families or colleagues; and user-POI visiting relations). Such relational information holds great potential to facilitate the next POI recommendation. However, most existing methods either focus on merely the user-POI visits, or handle different relations based on over-simplified assumptions while neglecting relational heterogeneities. To fill these critical voids, we propose a novel framework, MEMO, which effectively utilizes the heterogeneous relations with a multi-network representation learning module, and explicitly incorporates the inter-temporal user-POI mutual influence with the coupled recurrent neural networks. Extensive experiments on real-world LBSN data validate the superiority of our framework over the state-of-the-art next POI recommendation methods.
Contrastive Language-Action Pre-training for Temporal Localization
Venue: arXiv cs.CV 2022
Year: 2022

Long-form video understanding requires designing approaches that are able to temporally localize activities or language. End-to-end training for such tasks is limited by the compute device memory constraints and lack of temporal annotations at large-scale. These limitations can be addressed by pre-training on large datasets of temporally trimmed videos supervised by class annotations. Once the video encoder is pre-trained, it is common practice to freeze it during fine-tuning. Therefore, the video encoder does not learn temporal boundaries and unseen classes, causing a domain gap with respect to the downstream tasks. Moreover, using temporally trimmed videos prevents to capture the relations between different action categories and the background context in a video clip which results in limited generalization capacity. To address these limitations, we propose a novel post-pre-training approach without freezing the video encoder which leverages language. We introduce a masked contrastive learning loss to capture visio-linguistic relations between activities, background video clips and language in the form of captions. Our experiments show that the proposed approach improves the state-of-the-art on temporal action localization, few-shot temporal action localization, and video language grounding tasks.
Unsupervised Segmentation of Hyperspectral Remote Sensing Images with Superpixels
Venue: arXiv cs.CV 2022
Year: 2022

In this paper, we propose an unsupervised method for hyperspectral remote sensing image segmentation. The method exploits the mean-shift clustering algorithm that takes as input a preliminary hyperspectral superpixels segmentation together with the spectral pixel information. The proposed method does not require the number of segmentation classes as input parameter, and it does not exploit any a-priori knowledge about the type of land-cover or land-use to be segmented (e.g. water, vegetation, building etc.). Experiments on Salinas, SalinasA, Pavia Center and Pavia University datasets are carried out. Performance are measured in terms of normalized mutual information, adjusted Rand index and F1-score. Results demonstrate the validity of the proposed method in comparison with the state of the art.
Information Retrieval in Friction Stir Welding of Aluminum Alloys by using Natural Language Processing based Algorithms
Venue: arXiv cs.IR 2022
Year: 2022

Text summarization is a technique for condensing a big piece of text into a few key elements that give a general impression of the content. When someone requires a quick and precise summary of a large amount of information, it becomes vital. If done manually, summarizing text can be costly and time-consuming. Natural Language Processing (NLP) is the sub-division of Artificial Intelligence that narrows down the gap between technology and human cognition by extracting the relevant information from the pile of data. In the present work, scientific information regarding the Friction Stir Welding of Aluminum alloys was collected from the abstract of scholarly research papers. For extracting the relevant information from these research abstracts four Natural Language Processing based algorithms i.e. Latent Semantic Analysis (LSA), Luhn Algorithm, Lex Rank Algorithm, and KL-Algorithm were used. In order to evaluate the accuracy score of these algorithms, Recall-Oriented Understudy for Gisting Evaluation (ROUGE) was used. The results showed that the Luhn Algorithm resulted in the highest f1-Score of 0.413 in comparison to other algorithms.
Lattices Without a Big Constant and With Noise
Venue: arXiv cs.DS 2022
Year: 2022

We show how Frieze's analysis of subset sum solving using lattices can be done with out any large constants and without flipping. We apply the variant without the large constant to inputs with noise.
Restricted Black-box Adversarial Attack Against DeepFake Face Swapping
Venue: arXiv cs.CV 2022
Year: 2022

DeepFake face swapping presents a significant threat to online security and social media, which can replace the source face in an arbitrary photo/video with the target face of an entirely different person. In order to prevent this fraud, some researchers have begun to study the adversarial methods against DeepFake or face manipulation. However, existing works focus on the white-box setting or the black-box setting driven by abundant queries, which severely limits the practical application of these methods. To tackle this problem, we introduce a practical adversarial attack that does not require any queries to the facial image forgery model. Our method is built on a substitute model persuing for face reconstruction and then transfers adversarial examples from the substitute model directly to inaccessible black-box DeepFake models. Specially, we propose the Transferable Cycle Adversary Generative Adversarial Network (TCA-GAN) to construct the adversarial perturbation for disrupting unknown DeepFake systems. We also present a novel post-regularization module for enhancing the transferability of generated adversarial examples. To comprehensively measure the effectiveness of our approaches, we construct a challenging benchmark of DeepFake adversarial attacks for future development. Extensive experiments impressively show that the proposed adversarial attack method makes the visual quality of DeepFake face images plummet so that they are easier to be detected by humans and algorithms. Moreover, we demonstrate that the proposed algorithm can be generalized to offer face image protection against various face translation methods.
A survey on attention mechanisms for medical applications: are we moving towards better algorithms?
Venue: arXiv cs.CV 2022
Year: 2022

The increasing popularity of attention mechanisms in deep learning algorithms for computer vision and natural language processing made these models attractive to other research domains. In healthcare, there is a strong need for tools that may improve the routines of the clinicians and the patients. Naturally, the use of attention-based algorithms for medical applications occurred smoothly. However, being healthcare a domain that depends on high-stake decisions, the scientific community must ponder if these high-performing algorithms fit the needs of medical applications. With this motto, this paper extensively reviews the use of attention mechanisms in machine learning (including Transformers) for several medical applications. This work distinguishes itself from its predecessors by proposing a critical analysis of the claims and potentialities of attention mechanisms presented in the literature through an experimental case study on medical image classification with three different use cases. These experiments focus on the integrating process of attention mechanisms into established deep learning architectures, the analysis of their predictive power, and a visual assessment of their saliency maps generated by post-hoc explanation methods. This paper concludes with a critical analysis of the claims and potentialities presented in the literature about attention mechanisms and proposes future research lines in medical applications that may benefit from these frameworks.
Bifrost: End-to-End Evaluation and Optimization of Reconfigurable DNN Accelerators
Venue: arXiv cs.LG 2022
Year: 2022

Reconfigurable accelerators for deep neural networks (DNNs) promise to improve performance such as inference latency. STONNE is the first cycle-accurate simulator for reconfigurable DNN inference accelerators which allows for the exploration of accelerator designs and configuration space. However, preparing models for evaluation and exploring configuration space in STONNE is a manual developer-timeconsuming process, which is a barrier for research. This paper introduces Bifrost, an end-to-end framework for the evaluation and optimization of reconfigurable DNN inference accelerators. Bifrost operates as a frontend for STONNE and leverages the TVM deep learning compiler stack to parse models and automate offloading of accelerated computations. We discuss Bifrost's advantages over STONNE and other tools, and evaluate the MAERI and SIGMA architectures using Bifrost. Additionally, Bifrost introduces a module leveraging AutoTVM to efficiently explore accelerator designs and dataflow mapping space to optimize performance. This is demonstrated by tuning the MAERI architecture and generating efficient dataflow mappings for AlexNet, obtaining an average speedup of $50\times$ for the convolutional layers and $11\times$ for the fully connected layers. Our code is available at www.github.com/gicLAB/bifrost.
Frequency Plan Design for Multibeam Satellite Constellations Using Linear Programming
Venue: arXiv cs.CE 2022
Year: 2022

Upcoming large satellite constellations and the advent of tighter steerable beams will offer unprecedented flexibility. This new flexibility will require resource management strategies to be operated in high-dimensional and dynamic environments, as existing satellite operators are unaccustomed to operational flexibility and automation. Frequency assignment policies have the potential to drive constellations' performance in this new context, and are no exception to real-time and scalability requirements. The majority of frequency assignment methods proposed in the literature fail to fulfill these two requirements, or are unable to meet them without falling short on bandwidth and/or power efficiency. In this paper we propose a new frequency assignment method designed to prioritize operational requirements. We present an algorithm based on Integer Linear Programming (ILP) that is able to fully define a frequency plan while respecting key system constraints such as handovers and interference. We are able to encode operators' goals such as bandwidth maximization or power reduction and produce optimal or quasi-optimal plans according to such objectives. In our experiments, we find our method is able to allocate at least 50% more bandwidth and reduce power consumption by 40% compared to previous operational benchmarks. The performance advantage of our method compared to previous solutions increases with the dimensionality of the constellation; in an experiment with a 5,000-beam MEO constellation we find that we can allocate three times more bandwidth.
An Empirical Study of the Occurrence of Heavy-Tails in Training a ReLU Gate
Venue: arXiv cs.LG 2022
Year: 2022

A particular direction of recent advance about stochastic deep-learning algorithms has been about uncovering a rather mysterious heavy-tailed nature of the stationary distribution of these algorithms, even when the data distribution is not so. Moreover, the heavy-tail index is known to show interesting dependence on the input dimension of the net, the mini-batch size and the step size of the algorithm. In this short note, we undertake an experimental study of this index for S.G.D. while training a $\relu$ gate (in the realizable and in the binary classification setup) and for a variant of S.G.D. that was proven in Karmakar and Mukherjee (2022) for ReLU realizable data. From our experiments we conjecture that these two algorithms have similar heavy-tail behaviour on any data where the latter can be proven to converge. Secondly, we demonstrate that the heavy-tail index of the late time iterates in this model scenario has strikingly different properties than either what has been proven for linear hypothesis classes or what has been previously demonstrated for large nets.
Process Knowledge-infused Learning for Suicidality Assessment on Social Media
Venue: arXiv cs.AI 2022
Year: 2022

Improving the performance and natural language explanations of deep learning algorithms is a priority for adoption by humans in the real world. In several domains, such as healthcare, such technology has significant potential to reduce the burden on humans by providing quality assistance at scale. However, current methods rely on the traditional pipeline of predicting labels from data, thus completely ignoring the process and guidelines used to obtain the labels. Furthermore, post hoc explanations on the data to label prediction using explainable AI (XAI) models, while satisfactory to computer scientists, leave much to be desired to the end-users due to lacking explanations of the process in terms of human-understandable concepts. We \textit{introduce}, \textit{formalize}, and \textit{develop} a novel Artificial Intelligence (A) paradigm -- Process Knowledge-infused Learning (PK-iL). PK-iL utilizes a structured process knowledge that explicitly explains the underlying prediction process that makes sense to end-users. The qualitative human evaluation confirms through a annotator agreement of 0.72, that humans are understand explanations for the predictions. PK-iL also performs competitively with the state-of-the-art (SOTA) baselines.
Skyline Operators and Regret Minimization Techniques for Managing User Preferences in the Query Process
Venue: arXiv cs.DB 2022
Year: 2022

The problem of selecting the most representative tuples from a dataset has led to the development of powerful tools, among which Skyline and Ranking (or Top-k) queries stand out for their ability to support the optimization of multiple criteria in the query process. This paper surveys the remarkable efforts made towards the extension of the aforementioned tools to overcome their limitations, respectively the explosion of the output result and the difficulty of query formulation. Moreover, we explore the application of these state-of-the-art techniques as preference-based query frameworks, proposing a comparison of their query personalization capabilities, the ability to control the output size and their flexibility with respect to the user input preferences.
Zero-Touch Network on Industrial IoT: An End-to-End Machine Learning Approach
Venue: arXiv cs.LG 2022
Year: 2022

Industry 4.0-enabled smart factory is expected to realize the next revolution for manufacturers. Although artificial intelligence (AI) technologies have improved productivity, current use cases belong to small-scale and single-task operations. To unbound the potential of smart factory, this paper develops zero-touch network systems for intelligent manufacturing and facilitates distributed AI applications in both training and inferring stages in a large-scale manner. The open radio access network (O-RAN) architecture is first introduced for the zero-touch platform to enable globally controlling communications and computation infrastructure capability in the field. The designed serverless framework allows intelligent and efficient learning assignments and resource allocations. Hence, requested learning tasks can be assigned to appropriate robots, and the underlying infrastructure can be used to support the learning tasks without expert knowledge. Moreover, due to the proposed network system's flexibility, powerful AI-enabled networking algorithms can be utilized to ensure service-level agreements and superior performances for factory workloads. Finally, three open research directions of backward compatibility, end-to-end enhancements, and cybersecurity are discussed for zero-touch smart factory.
A 3-Approximation Algorithm for a Particular Case of the Hamiltonian p-Median Problem
Venue: arXiv cs.DS 2022
Year: 2022

Given a weighted graph $G$ with $n$ vertices and $m$ edges, and a positive integer $p$, the Hamiltonian $p$-median problem consists in finding $p$ cycles of minimum total weight such that each vertex of $G$ is in exactly one cycle. We introduce an $O(n^6)$ 3-approximation algorithm for the particular case in which $p \leq \lceil \frac{n-2\lceil \frac{n}{5} \rceil}{3} \rceil$. An approximation ratio of 2 might be obtained depending on the number of components in the optimal 2-factor of $G$. We present computational experiments comparing the approximation algorithm to an exact algorithm from the literature. In practice much better ratios are obtained. For large values of $p$, the exact algorithm is outperformed by our approximation algorithm.
From Sand to Flour: The Next Leap in Granular Computing with NanoSort
Venue: arXiv cs.DC 2022
Year: 2022

The granularity of distributed computing is limited by communication time: there is no point in farming out smaller and smaller tasks if the communication overhead dominates the decrease in processing time due to the added parallelism. In this work, we leverage the low communication latency of a new NIC/CPU hardware design, the nanoPU, to explore a new extreme of granularity in distributed computation, where a problem is partitioned into tens of thousands of nanosecond-scale tasks. To evaluate the feasibility and practicality of extremely fine-grained computing, we built NanoSort, a distributed sorting algorithm running on the nanoPU. Using cycle-accurate FireSim simulations of 65,536 nanoPU cores, we show that NanoSort can sort 1M keys in 68$μ$s, an order of magnitude faster than MilliSort, the current state-of-the-art.
Spread of COVID-19: Adult Detention Facilities in LA County
Venue: arXiv cs.DC 2022
Year: 2022

We analyze the spread of COVID-19 cases within adult detention facilities in Los Angeles (LA) county. Throughout the analysis we review the data to explore the range of positive cases in each center and see the percentage of people who were positive for COVID-19 against the amount of people who were tested. Additionally, we see if there is any correlation between the surrounding community of each detention center and the number of positive cases in each center and explore the protocols in place at each detention center. We use the cloud visualization tool SAP Analytics Cloud (SAC) with the data from the California government website through adult detention facilities in LA County. We found that (1) the number of confirmed cases at the facilities and the surrounding communities are not related, (2) the data does not represent all positive cases at the facility, and (3) there are not enough tests at the facilities.